def group_analysis(design, contrast):
""" Compute group analysis effect, t, sd for `design` and `contrast`
Saves to disk in 'group' analysis directory
Parameters
----------
design : {'block', 'event'}
contrast : str
contrast name
"""
array = np.array # shorthand
# Directory where output will be written
odir = futil.ensure_dir(futil.DATADIR, 'group', design, contrast)
# Which subjects have this (contrast, design) pair?
subj_con_dirs = futil.subj_des_con_dirs(design, contrast)
if len(subj_con_dirs) == 0:
raise ValueError('No subjects for %s, %s' % (design, contrast))
# Assemble effects and sds into 4D arrays
sds = []
Ys = []
for s in subj_con_dirs:
sd_img = load_image(pjoin(s, "sd.nii"))
effect_img = load_image(pjoin(s, "effect.nii"))
sds.append(sd_img.get_data())
Ys.append(effect_img.get_data())
sd = array(sds)
Y = array(Ys)
# This function estimates the ratio of the fixed effects variance
# (sum(1/sd**2, 0)) to the estimated random effects variance
# (sum(1/(sd+rvar)**2, 0)) where rvar is the random effects variance.
# The EM algorithm used is described in:
#
# Worsley, K.J., Liao, C., Aston, J., Petre, V., Duncan, G.H.,
# Morales, F., Evans, A.C. (2002). \'A general statistical
# analysis for fMRI data\'. NeuroImage, 15:1-15
varest = onesample.estimate_varatio(Y, sd)
random_var = varest['random']
# XXX - if we have a smoother, use
# random_var = varest['fixed'] * smooth(varest['ratio'])
# Having estimated the random effects variance (and possibly smoothed it),
# the corresponding estimate of the effect and its variance is computed and
# saved.
# This is the coordmap we will use
coordmap = futil.load_image_fiac("fiac_00", "wanatomical.nii").coordmap
adjusted_var = sd**2 + random_var
adjusted_sd = np.sqrt(adjusted_var)
results = onesample.estimate_mean(Y, adjusted_sd)
for n in ['effect', 'sd', 't']:
im = api.Image(results[n], copy(coordmap))
save_image(im, pjoin(odir, "%s.nii" % n))
def view_thresholdedT(design, contrast, threshold, inequality=np.greater):
"""
A mayavi isosurface view of thresholded t-statistics
Parameters
----------
design : {'block', 'event'}
contrast : str
threshold : float
inequality : {np.greater, np.less}, optional
"""
maska = np.asarray(MASK)
tmap = np.array(load_image_fiac('group', design, contrast, 't.nii'))
test = inequality(tmap, threshold)
tval = np.zeros(tmap.shape)
tval[test] = tmap[test]
# XXX make the array axes agree with mayavi2
avganata = np.array(AVGANAT)
avganat_iso = mlab.contour3d(avganata * maska, opacity=0.3, contours=[3600],
color=(0.8,0.8,0.8))
avganat_iso.actor.property.backface_culling = True
avganat_iso.actor.property.ambient = 0.3
tval_iso = mlab.contour3d(tval * MASK, color=(0.8,0.3,0.3),
contours=[threshold])
return avganat_iso, tval_iso
def group_analysis(design, contrast):
""" Compute group analysis effect, t, sd for `design` and `contrast`
Saves to disk in 'group' analysis directory
Parameters
----------
design : {'block', 'event'}
contrast : str
contrast name
"""
array = np.array # shorthand
# Directory where output will be written
odir = futil.ensure_dir(futil.DATADIR, 'group', design, contrast)
# Which subjects have this (contrast, design) pair?
subj_con_dirs = futil.subj_des_con_dirs(design, contrast)
if len(subj_con_dirs) == 0:
raise ValueError('No subjects for %s, %s' % (design, contrast))
# Assemble effects and sds into 4D arrays
sds = []
Ys = []
for s in subj_con_dirs:
sd_img = load_image(pjoin(s, "sd.nii"))
effect_img = load_image(pjoin(s, "effect.nii"))
sds.append(sd_img.get_data())
Ys.append(effect_img.get_data())
sd = array(sds)
Y = array(Ys)
# This function estimates the ratio of the fixed effects variance
# (sum(1/sd**2, 0)) to the estimated random effects variance
# (sum(1/(sd+rvar)**2, 0)) where rvar is the random effects variance.
# The EM algorithm used is described in:
#
# Worsley, K.J., Liao, C., Aston, J., Petre, V., Duncan, G.H.,
# Morales, F., Evans, A.C. (2002). \'A general statistical
# analysis for fMRI data\'. NeuroImage, 15:1-15
varest = onesample.estimate_varatio(Y, sd)
random_var = varest['random']
# XXX - if we have a smoother, use
# random_var = varest['fixed'] * smooth(varest['ratio'])
# Having estimated the random effects variance (and possibly smoothed it),
# the corresponding estimate of the effect and its variance is computed and
# saved.
# This is the coordmap we will use
coordmap = futil.load_image_fiac("fiac_00","wanatomical.nii").coordmap
adjusted_var = sd**2 + random_var
adjusted_sd = np.sqrt(adjusted_var)
results = onesample.estimate_mean(Y, adjusted_sd)
for n in ['effect', 'sd', 't']:
im = api.Image(results[n], copy(coordmap))
save_image(im, pjoin(odir, "%s.nii" % n))
def fixed_effects(subj, design):
""" Fixed effects (within subject) for FIAC model
Finds run by run estimated model results, creates fixed effects results
image per subject.
Parameters
----------
subj : int
subject number 0..15 inclusive
design : {'block', 'event'}
design type
"""
# First, find all the effect and standard deviation images
# for the subject and this design type
path_dict = futil.path_info_design(subj, design)
rootdir = path_dict['rootdir']
# The output directory
fixdir = pjoin(rootdir, "fixed")
# Fetch results images from run estimations
results = futil.results_table(path_dict)
# Get our hands on the relevant coordmap to save our results
coordmap = futil.load_image_fiac("fiac_%02d" % subj,
"wanatomical.nii").coordmap
# Compute the "fixed" effects for each type of contrast
for con in results:
fixed_effect = 0
fixed_var = 0
for effect, sd in results[con]:
effect = load_image(effect).get_data()
sd = load_image(sd).get_data()
var = sd ** 2
# The optimal, in terms of minimum variance, combination of the
# effects has weights 1 / var
#
# XXX regions with 0 variance are set to 0
# XXX do we want this or np.nan?
ivar = np.nan_to_num(1. / var)
fixed_effect += effect * ivar
fixed_var += ivar
# Now, compute the fixed effects variance and t statistic
fixed_sd = np.sqrt(fixed_var)
isd = np.nan_to_num(1. / fixed_sd)
fixed_t = fixed_effect * isd
# Save the results
odir = futil.ensure_dir(fixdir, con)
for a, n in zip([fixed_effect, fixed_sd, fixed_t],
['effect', 'sd', 't']):
im = api.Image(a, copy(coordmap))
save_image(im, pjoin(odir, '%s.nii' % n))
def fixed_effects(subj, design):
""" Fixed effects (within subject) for FIAC model
Finds run by run estimated model results, creates fixed effects results
image per subject.
Parameters
----------
subj : int
subject number 0..15 inclusive
design : {'block', 'event'}
design type
"""
# First, find all the effect and standard deviation images
# for the subject and this design type
path_dict = futil.path_info_design(subj, design)
rootdir = path_dict['rootdir']
# The output directory
fixdir = pjoin(rootdir, "fixed")
# Fetch results images from run estimations
results = futil.results_table(path_dict)
# Get our hands on the relevant coordmap to save our results
coordmap = futil.load_image_fiac("fiac_%02d" % subj,
"wanatomical.nii").coordmap
# Compute the "fixed" effects for each type of contrast
for con in results:
fixed_effect = 0
fixed_var = 0
for effect, sd in results[con]:
effect = load_image(effect).get_data()
sd = load_image(sd).get_data()
var = sd**2
# The optimal, in terms of minimum variance, combination of the
# effects has weights 1 / var
#
# XXX regions with 0 variance are set to 0
# XXX do we want this or np.nan?
ivar = np.nan_to_num(1. / var)
fixed_effect += effect * ivar
fixed_var += ivar
# Now, compute the fixed effects variance and t statistic
fixed_sd = np.sqrt(fixed_var)
isd = np.nan_to_num(1. / fixed_sd)
fixed_t = fixed_effect * isd
# Save the results
odir = futil.ensure_dir(fixdir, con)
for a, n in zip([fixed_effect, fixed_sd, fixed_t],
['effect', 'sd', 't']):
im = api.Image(a, copy(coordmap))
save_image(im, pjoin(odir, '%s.nii' % n))
def group_analysis(design, contrast):
"""
Compute group analysis effect, sd and t
for a given contrast and design type
"""
array = np.array # shorthand
# Directory where output will be written
odir = futil.ensure_dir(futil.DATADIR, 'group', design, contrast)
# Which subjects have this (contrast, design) pair?
subjects = futil.subject_dirs(design, contrast)
sd = array([array(load_image(pjoin(s, "sd.nii"))) for s in subjects])
Y = array([array(load_image(pjoin(s, "effect.nii"))) for s in subjects])
# This function estimates the ratio of the
# fixed effects variance (sum(1/sd**2, 0))
# to the estimated random effects variance
# (sum(1/(sd+rvar)**2, 0)) where
# rvar is the random effects variance.
# The EM algorithm used is described in
#
# Worsley, K.J., Liao, C., Aston, J., Petre, V., Duncan, G.H.,
# Morales, F., Evans, A.C. (2002). \'A general statistical
# analysis for fMRI data\'. NeuroImage, 15:1-15
varest = onesample.estimate_varatio(Y, sd)
random_var = varest['random']
# XXX - if we have a smoother, use
# random_var = varest['fixed'] * smooth(varest['ratio'])
# Having estimated the random effects variance (and
# possibly smoothed it), the corresponding
# estimate of the effect and its variance is
# computed and saved.
# This is the coordmap we will use
coordmap = futil.load_image_fiac("fiac_00","wanatomical.nii").coordmap
adjusted_var = sd**2 + random_var
adjusted_sd = np.sqrt(adjusted_var)
results = onesample.estimate_mean(Y, adjusted_sd)
for n in ['effect', 'sd', 't']:
im = api.Image(results[n], coordmap.copy())
save_image(im, pjoin(odir, "%s.nii" % n))
def fixed_effects(subj, design):
"""
Fixed effects (within subject) for FIAC model
"""
# First, find all the effect and standard deviation images
# for the subject and this design type
path_dict = futil.path_info2(subj, design)
rootdir = path_dict['rootdir']
# The output directory
fixdir = pjoin(rootdir, "fixed")
results = futil.results_table(path_dict)
# Get our hands on the relevant coordmap to
# save our results
coordmap = futil.load_image_fiac("fiac_%02d" % subj,
"wanatomical.nii").coordmap
# Compute the "fixed" effects for each type of contrast
for con in results:
fixed_effect = 0
fixed_var = 0
for effect, sd in results[con]:
effect = load_image(effect); sd = load_image(sd)
var = np.array(sd)**2
# The optimal, in terms of minimum variance, combination of the
# effects has weights 1 / var
#
# XXX regions with 0 variance are set to 0
# XXX do we want this or np.nan?
ivar = np.nan_to_num(1. / var)
fixed_effect += effect * ivar
fixed_var += ivar
# Now, compute the fixed effects variance and t statistic
fixed_sd = np.sqrt(fixed_var)
isd = np.nan_to_num(1. / fixed_sd)
fixed_t = fixed_effect * isd
# Save the results
odir = futil.ensure_dir(fixdir, con)
for a, n in zip([fixed_effect, fixed_sd, fixed_t],
['effect', 'sd', 't']):
im = api.Image(a, coordmap.copy())
save_image(im, pjoin(odir, '%s.nii' % n))
try:
from mayavi import mlab
except ImportError:
try:
from enthought.mayavi import mlab
except ImportError:
raise RuntimeError('Need mayavi for this module')
from fiac_util import load_image_fiac
#-----------------------------------------------------------------------------
# Globals
#-----------------------------------------------------------------------------
MASK = load_image_fiac('group', 'mask.nii')
AVGANAT = load_image_fiac('group', 'avganat.nii')
#-----------------------------------------------------------------------------
# Functions
#-----------------------------------------------------------------------------
def view_thresholdedT(design, contrast, threshold, inequality=np.greater):
"""
A mayavi isosurface view of thresholded t-statistics
Parameters
----------
design : {'block', 'event'}
contrast : str
threshold : float
import fiac_util as futil
reload(futil) # while developing interactively
#-----------------------------------------------------------------------------
# Globals
#-----------------------------------------------------------------------------
SUBJECTS = tuple(range(5) + range(6, 16)) # No data for subject 5
RUNS = tuple(range(1, 5))
DESIGNS = ('event', 'block')
CONTRASTS = ('speaker_0', 'speaker_1',
'sentence_0', 'sentence_1',
'sentence:speaker_0',
'sentence:speaker_1')
GROUP_MASK = futil.load_image_fiac('group', 'mask.nii')
TINY_MASK = np.zeros(GROUP_MASK.shape, np.bool)
TINY_MASK[30:32,40:42,30:32] = 1
#-----------------------------------------------------------------------------
# Public functions
#-----------------------------------------------------------------------------
# For group analysis
def run_model(subj, run):
"""
Single subject fitting of FIAC model
"""
#----------------------------------------------------------------------
# Set initial parameters of the FIAC dataset
# Local
import fiac_util as futil
reload(futil) # while developing interactively
# -----------------------------------------------------------------------------
# Globals
# -----------------------------------------------------------------------------
SUBJECTS = tuple(range(5) + range(6, 16)) # No data for subject 5
RUNS = tuple(range(1, 5))
DESIGNS = ("event", "block")
CONTRASTS = ("speaker_0", "speaker_1", "sentence_0", "sentence_1", "sentence:speaker_0", "sentence:speaker_1")
GROUP_MASK = futil.load_image_fiac("group", "mask.nii")
TINY_MASK = np.zeros(GROUP_MASK.shape, np.bool)
TINY_MASK[30:32, 40:42, 30:32] = 1
# -----------------------------------------------------------------------------
# Public functions
# -----------------------------------------------------------------------------
# For group analysis
def run_model(subj, run):
"""
Single subject fitting of FIAC model
"""
# ----------------------------------------------------------------------
# Local
import fiac_util as futil
reload(futil) # while developing interactively
#-----------------------------------------------------------------------------
# Globals
#-----------------------------------------------------------------------------
SUBJECTS = tuple(range(5) + range(6, 16)) # No data for subject 5
RUNS = tuple(range(1, 5))
DESIGNS = ('event', 'block')
CONTRASTS = ('speaker_0', 'speaker_1', 'sentence_0', 'sentence_1',
'sentence:speaker_0', 'sentence:speaker_1')
GROUP_MASK = futil.load_image_fiac('group', 'mask.nii')
TINY_MASK = np.zeros(GROUP_MASK.shape, np.bool)
TINY_MASK[30:32, 40:42, 30:32] = 1
#-----------------------------------------------------------------------------
# Public functions
#-----------------------------------------------------------------------------
# For group analysis
def run_model(subj, run):
"""
Single subject fitting of FIAC model
"""
#----------------------------------------------------------------------